Recent advances in remote sensing of vegetation phenology: Retrieval algorithm and validation strategy
- Publisher:
- Aerospace Information Research Institute, Chinese Academy of Sciences
- Publication Type:
- Journal Article
- Citation:
- National Remote Sensing Bulletin, 2022, 26, (3), pp. 431-455
- Issue Date:
- 2022-03-25
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| Filename | Description | Size | |||
|---|---|---|---|---|---|
| 植被物候参数遥感提取与验证方法研究进展_NormalPdf.pdf | Published version | 936.83 kB |
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In the context of climate change, vegetation phenology, as a direct manifestation of the ecosystem's response to environmental changes, has attracted increasing attention from the academic community. Obtaining long-term, continuous, multi-scale vegetation phenology data is the basis of phenological research, and the phenological parameters obtained by satellite remote sensing have become an important indicator of terrestrial ecosystem change. Remote sensing phenological parameters play an important role in the fields of agricultural production management, ecosystem monitoring, land use type mapping, human health, and ecosystem climate change response. In this context, key scientific issues and application fields must be combined to systematically sort out the progress in remote sensing phenological parameter extraction, verification, and product development and to predict to future development trends. First, this article discussed the development of emerging sunlight-induced chlorophyll fluorescence and vegetation optical thickness in addition to the traditional vegetation indices in phenological monitoring. Second, this paper discusses the advantages, disadvantages, and applicability of different time series data preprocessing and phenological metrics retrieval algorithms. Then, this article sorts out the development context of multi-source and -scale verification methods from the development of traditional phenological observations, phenological cameras, flux observations, and unmanned aerial vehicles. Meanwhile, this article introduces the development status of domestic and foreign phenology remote sensing products in recent years, with emphasis on product accuracy. Finally, this article systematically discusses the propagation of errors to the retrieved phenological metrics resulting from different aspects of data preprocessing, parameter extraction methods, and remote sensing data sources. On this basis, this article points out that future research in the field of vegetation phenology remote sensing should focus on the following: (1) better comparability between different research results should be targeted by improving the quality of remote sensing data sources and spatial and temporal consistency; (2) the subjectivity in the phenological retrieval algorithms should be reduced by developing universal algorithms; (3) the complete ground validation scheme should be established by leveraging the development in theory and method of quantitative remote sensing validation field; (4) the experience of using Chinese satellite data for monitoring vegetation phenology should be accumulated by actively extending the application of different Chinese spaceborne sensors. Through the above development, the overarching aim is to meet the demand for high-quality vegetation phenology remote sensing products in various scientific and practical applications.
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